Infertility affects more than 80 million people worldwide. It is estimated that 10% of all couples experience primary or secondary infertility (Vayena et al. 2001). In vitro fertilization (IVF) is an elective medical treatment that may provide a couple who has been otherwise unable to conceive a chance to establish a pregnancy. It is a process in which eggs (oocytes) are taken from a woman's ovaries and then fertilized with sperm in the laboratory. The embryos created in this process are then placed into the uterus for potential implantation. To avoid multiple pregnancies and multiple births only a few embryos are transferred (normally less than four and ideally only one (Bhattacharya et al. 2004)). Selecting proper embryos for transfer is a critical step in any IVF-treatment. Current selection procedures are mostly entirely based on morphological evaluation of the embryo at different timepoints during development and particularly an evaluation at the time of transfer using a standard stereomicroscope. However, it is widely recognized that the evaluation procedure needs qualitative as well as quantitative improvements.
Early cell division. A promising new approach is to use ‘early division’ to the 2-cell stage, (i.e. before 25-27 h post insemination/injection), as a quality indicator. In this approach the embryos are visually inspected 25-27 hours after fertilization to determine if the first cell division has been completed. Several studies have demonstrated strong correlation between early cleavage and subsequent development potential of individual embryos. (Shoukir et al., 1997; Sakkas et al., 1998, 2001; Bos-Mikich et al., 2001; Lundin et al., 2001; Petersen et al., 2001; Fenwick et al., 2002; Neuber et al. 2003; Salumets et al., 2003; Windt et al., 2004). The need for more frequent observation has been pointed out by several observers, however, frequent visual observations with associated transfers from the incubator to an inverted microscope induces a physical stress that may impede or even stall embryo development. It is also time consuming and difficult to incorporate in the daily routine of IVF clinics.
Several researchers have performed time-lapse image acquisition during embryo development. This has mainly been done by placing a research microscope inside an incubator or building an “incubator stage” onto a microscope stage with automated image acquisition. The “incubator” maintain acceptable temperature (37° C.), humidity (>90%) and gas composition (5% CO2 and in some cases reduced oxygen concentration). Manual assessment of time-lapse images has yielded important information about timing and duration of cell divisions (Grisart et al. 1994, Holm et al. 1998, Majerus et al. 2000, Holm et al. 2002, Holm et al. 2003, Lequarre et al. 2003, Motosugi et al. 2005).
An alternative experimental setup involves placing an image acquisition system inside an incubator to observe the embryos during development without stressing them by moving them outside the optimized conditions inside the incubator. A commercial system the EmbryoGuard is being manufactured and sold by IMT international (see literature list.) In this setup it is possible to observe the embryos online inside the incubator.
Conventional image analysis. Morphological scoring of embryo images and time-lapse videos of embryo development have relied on manual analysis where the viewer gives grades to picture and the computer only keep track of this grading, generating an annotated timeline showing when major changes occurred. An example of such software is the annotation software provided with the time-lapse image acquisition system of EmbryoGuard. Examples of manual analysis of time-lapse videos can be found in Grisart et al. 1994, Holm et al. 1998, Majerus et al. 2000, Holm et al. 2002, Holm et al. 2003, Lequarre et al. 2003, Motosugi et al. 2005.
Current software for quantitative image analysis of embryo pictures employs a semiautomatic or computer assisted algorithm. This is a computer aided image scoring where the user uses drawing tools to delineate embryonic structures that are subsequently quantified based on the user derived outline of these. Several programs to perform semiautomatic analysis of embryo pictures are commercially available (e.g. FertiMorph from ImageHouse, Copenhagen, Denmark). Several attempts have been made to make a fully automated analysis system (e.g. PhD thesis of Christina Hnida) for embryo pictures. However, the general use of Hoffmann modulation contrast (HMC) images in embryology and IVF laboratories have made automatic cell detection difficult.
Automated image analysis has been developed for other applications such as detection of mitotic cells in cell cultures (Eccles et al. 1986, Klevecz et al. 1988 U.S. Pat. No. 4,724,543, Belien et al. 1997, and Curl et al. 2004). All the reported automatic algorithms use the classical scheme for quantitative image analysis:                1. Acquire image        2. Enhance image        3. Segment image into regions of interest (ROI's) by thresholding        4. Count and characterize the ROI's (size, density etc.)        
A general description of these steps and numerous variants of each can be found in review articles and textbooks on image analysis (e.g. the review of histological image analysis by Oberholzer et al. 1996 or The Image Processing Handbook, 4th Ed. 2002 by John Russ). The best methods to enhance the presentation of structures of interest depend on the image at hand (e.g. microscopy pictures) and the representation of the structure of interest (e.g. nuclei), and many different variants have been used. However, enhancement procedures are always used to facilitate segmentation of the picture in order to identify and delineate regions of interest Once these regions have been found and identified they can be characterized further with respect to area, size, intensity, position etc.
The segmentation itself is accomplished by comparing the pixel intensity (or a function derived from the pixel intensity) to a given threshold. Areas above the threshold belong to the region of interest (ROI) which is usually the object (e.g. nuclei) that must be measured. Numerous different algorithms for this segmentation are used but they always serve the same purpose i.e. a segmentation of the image.
Automated analysis of time-lapse microscopy images to detect cell division is presented in a paper by Eccles et al. 1986. The paper describes a method for automated detection of cell division in synchronized mammalian cells by analysis of images in a time-lapse series. The image analysis algorithm described and used in this paper does not use intensity differences in consecutive frames. Instead it analyses each image by first extracting high-frequency picture components, then thresholding and probing for annular objects indicative of putative mitotic cells. This operation constitutes a segmentation of the image to detect the cell outlines and their relative position. Spatial and temporal relationships between annuli on consecutive frames were examined to discern the occurrence of mitoses.
Another approach is presented in U.S. Pat. No. 23,185,450 to analyze the image sequence by using a self-similarity matrix method. The matrix consists of normalized pairwise similarity values. This method is used to analyze the long and short term similarities between frames.
All patent and non-patent references cited in the application, or in the present application, are also hereby incorporated by reference in their entirety.